Numerous techniques for merchandising items have been developed for application in an online environment. In one technique, a Web merchandiser sends recommendations to users via the Internet. The recommendations typically prompt the users to purchase items identified by the recommendations, such as products (e.g., books, music, clothing, jewelry, etc.) and/or services.
To increase the effectiveness of the recommendations, and hence sales revenue, the Web merchandiser may attempt to provide recommendations which match the interests of the users. To ascertain the interests of the users, the Web merchandiser may examine the online behavior of the users. For example, the Web merchandiser may examine the online purchases of the users and/or other online activity of the users. A user who purchases an item (or performs some other action with respect to the item) is presumed to have an interest in this kind of item.
In generating recommendations, the Web merchandiser can then use a recommendation engine to determine items which are related to the assessed interests of the users. The recommendation engine can assess “relatedness” in different ways. In one technique, the recommendation engine can rely on empirical data derived from user profiles to gauge similarity. For example, the recommendation engine can assess that item X is similar to item Y if a statistically significant number of users who purchased item X also purchased item Y. More generally, recommendation engines are often based on the implicit assumption that a recommendation's value is directly proportional to its relatedness to the assessed interests of a user. Thus, the objective of these recommendation engines is to generate a set of n recommendations which most closely match the assessed interests of the user.
Such an approach has proven successful, but it is not without its drawbacks. For example, consider the scenario shown in FIG. 1. In this case, a user, John Smith, has purchased one or more books by the author Dostoevsky. Based on this pattern of behavior, the recommendation engine logically determines that this user is interested in books by Dostoevsky. In response, the recommendation engine may determine that the most relevant recommendations correspond to additional books written by Dostoevsky (that the user has not yet purchased). This scenario reflects the situation in FIG. 1, where the recommendation engine generates recommendations 102, 104, 106, 108, etc., which all pertain to books written by the author Dostoevsky.
FIG. 2 illustrates the operation of the recommendation engine in high-level conceptual form. In this figure, the source information 202 serves as the basis from which recommendation are generated. This source information 202, for instance, may reflect the user's prior purchase of several books by Dostoevsky. The items R1-R16 which figuratively “orbit” the source information 202 correspond to recommendations generated by the recommendation engine based on information provided by the source information 202. The “distance” of each recommendation from the source information 202 represents the assessed relatedness between that recommendation and the source information 202. For instance, recommendation R2 is more related to the source information 202 than recommendation R12, because the distance d1 between the recommendation R2 and the source information 202 is shorter than the distance d2 between the recommendation R12 and the source information 202. As explained above, the goal of many recommendation engines is to select a set of n recommendations within a certain threshold distance from the source information 202, defined by a core similarity space 204. Recommendations 102-108 shown in FIG. 1 correspond to recommendations R1-R4 within the core similarity space 204.
The recommendations 102-108 shown in FIG. 1 no doubt match one of John Smith's literary interests. However, the recommendations 102-108 may not convey sufficiently interesting information to this user. This is because a user who has purchased several books by Dostoevsky likely knows something about Dostoevsky, including at least the identity of the most popular works written by this author. Thus, alerting the user to the existence of other works by Dostoevsky likely does not impart any novel information to the user. And even if the user is actually interested in books by Dostoevsky, the homogeneous nature of these recommendations may quickly lose the interest of the user.
Therefore, known recommendation-generating algorithms may, in some circumstances, prove unsatisfactory for at least two reasons. First, inundating a user with many recommendations pertaining to an obvious or “univocal” theme might displease the user, as this approach essentially clutters the user's user interface presentation with useless or monotonous information. Second, providing obvious and/or highly homogenous recommendations to the user may fail to prompt the user to purchase the recommended items, thus potentially reducing the revenue of the Web merchandiser.
For at least the above-identified reasons, there is an exemplary need for more satisfactory strategies for providing recommendations.